Weight approximations in multi-attribute decision models

The use of surrogate weights based on rankings has been proposed as a method for avoiding difficulties associated with the elicitation of weights in multi-attribute decision analysis. When the simple multiattribute rating technique using swings (SMARTS) method is being employed it has been suggested that rank order centroid (ROC) weights are the best surrogate weights to use. This study shows that ROC weights are appropriate to use as a substitute for original weights that are constrained to sum to a fixed total (usually 1 or 100) as used in the point allocation method. If, however, the original weights are determined without any initial restrictions, as in the direct rating method, and are then normalized, which is the common procedure in SMARTS analysis, then the ROC weights do not provide the best approximations to the original weights. This paper shows how to obtain rank order distribution (ROD) weights that provide a better approximation than the ROC approach to unrestricted original weights. The paper also shows that, as the number of attributes in a decision problem increases, the ROD weights approximate to the more easily calculated rank sum weights.